Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation

Mohammad S. E. Sendi, Allison C. Waters, Vineet Tiruvadi, Patricio Riva-Posse, Andrea Crowell, Faical Isbaine, John T. Gale, Ki Sueng Choi, Robert E. Gross, Helen S. Mayberg & Babak Mahmoudi

Icahn School of Medicine at Mount Sinai, Emory University School of Medicine

 Translational Psychiatry volume 11, Article number: 551 (2021)

Abstract

Eight patients underwent intraoperative electrophysiological recording when bilateral DBS leads were implanted in the SCC using a connectomic approach at the site previously shown to optimize 6-month treatment outcomes. A machine learning classification method was used to discriminate between intracranial local field potentials (LFPs) recorded at baseline (stimulation-naïve) and after the first exposure to SCC DBS during surgical procedures. Spectral inputs (theta, 4–8 Hz; alpha, 9–12 Hz; beta, 13–30 Hz) to the model were then evaluated for importance to classifier success and tested as predictors of the antidepressant response. A decline in depression scores by 45.6% was observed after 1 week and this early antidepressant response correlated with a decrease in SCC LFP beta power, which most contributed to classifier success. Intraoperative exposure to therapeutic stimulation may result in an acute decrease in symptoms of depression following SCC DBS surgery. The correlation of symptom improvement with an intraoperative reduction in SCC beta power suggests this electrophysiological finding as a biomarker for treatment optimization.

Subject terms: Predictive markers, Neuroscience, Depression